Gastos Casa
23 September, 2024
1. Gastos (cálculos antiguos)
Gastos_casa %>%
dplyr::select(-Tiempo,-link) %>%
dplyr::select(fecha, gasto, monto, gastador,obs) %>% tail(30) %>%
knitr::kable(format = "markdown", size=12)| fecha | gasto | monto | gastador | obs |
|---|---|---|---|---|
| 26/7/2024 | Gas | 72850 | Andrés | NA |
| 27/7/2024 | Comida | 4300 | Andrés | biblioteca san camilo |
| 27/7/2024 | Comida | 27300 | Andrés | NA |
| 28/7/2024 | Comida | 67250 | Tami | Supermercado |
| 30/7/2024 | Comida | 30650 | Andrés | piwén |
| 1/8/2024 | Electricidad | 209425 | Andrés | NA |
| 3/8/2024 | Comida | 45130 | Tami | Supermercado |
| 3/8/2024 | Comida | 19490 | Tami | Barritas Wild Soul |
| 11/8/2024 | Comida | 8230 | Tami | Supermercado |
| 8/8/2024 | Comida | 5000 | Andrés | platano y cosas |
| 15/8/2024 | Electricidad | 190084 | Andrés | enel rqlo (dupliqué el gasto para que pagues eso. Yo ya ni uso mi estufa, y tal vez tú deberías usar la mía porque tiene temporizador) |
| 18/8/2024 | VTR | 21990 | Andrés | NA |
| 16/8/2024 | Comida | 11756 | Andrés | lider |
| 19/8/2024 | Comida | 91135 | Tami | Supermercado |
| 23/8/2024 | Otros | 170000 | Andrés | instalacion |
| 23/8/2024 | Otros | 285000 | Andrés | aire |
| 25/8/2024 | Gas | 75000 | Andrés | NA |
| 25/8/2024 | Comida | 66781 | Tami | Supermercado |
| 29/8/2024 | Comida | 45000 | Andrés | piwen |
| 2/9/2024 | Comida | 42057 | Tami | Supermercado |
| 4/9/2024 | Agua | 11723 | Andrés | NA |
| 4/9/2024 | Comida | 19490 | Tami | Barritas Wild Soul |
| 7/9/2024 | Comida | 15960 | Tami | Supermercado |
| 8/9/2024 | Gas/Bencina | 20182 | Tami | Parafina |
| 8/9/2024 | Comida | 55162 | Tami | Supermercado |
| 14/9/2024 | Comida | 16000 | Andrés | NA |
| 14/9/2024 | Comida | 59823 | Tami | Supermercado |
| 20/9/2024 | Electricidad | 22000 | Andrés | NA |
| 31/3/2019 | Comida | 9000 | Andrés | NA |
| 8/9/2019 | Comida | 24588 | Andrés | Super Lider |
#para ver las diferencias depués de la diosi
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::group_by(gastador, fecha,.drop = F) %>%
dplyr::summarise(gasto_media=mean(monto,na.rm=T)) %>%
dplyr::mutate(treat=ifelse(fecha>"2019-W26",1,0)) %>%
#dplyr::mutate(fecha_simp=lubridate::week(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
assign("ts_gastos_casa_week_treat", ., envir = .GlobalEnv)
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Promedio de gasto por gastador", data=ts_gastos_casa_week_treat,ylim=c(0,75000), xlab="", ylab="")
par(mfrow=c(1,2))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Antes de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==0,], xlab="", ylab="", ylim=c(0,70000))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Después de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==1,], xlab="", ylab="",ylim=c(0,70000))
library(ggiraph)
library(scales)
#if( requireNamespace("dplyr", quietly = TRUE)){
gg <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(treat=ifelse(fecha_week>"2019 W26",1,0)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
# dplyr::mutate(week=as.Date(as.character(lubridate::floor_date(fecha, "week"))))%>%
#dplyr::mutate(fecha_week= lubridate::parse_date_time(fecha_week, c("%Y-W%V"),exact=T)) %>%
dplyr::group_by(gastador_nombre, fecha_simp) %>%
dplyr::summarise(monto_total=sum(monto)) %>%
dplyr::mutate(tooltip= paste0(substr(gastador_nombre,1,1),"=",round(monto_total/1000,2))) %>%
ggplot(aes(hover_css = "fill:none;")) +#, ) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre)),size=1,alpha=.5) +
ggiraph::geom_point_interactive(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre),tooltip=tooltip),size = 1) +
#geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
# guides(color = F)+
theme_custom() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") + ggtitle( "Figura 4. Gastos por Gastador") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(date_breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35), legend.position='bottom')+
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
# x <- girafe(ggobj = gg)
# x <- girafe_options(x = x,
# opts_hover(css = "stroke:red;fill:orange") )
# if( interactive() ) print(x)
#}
tooltip_css <- "background-color:gray;color:white;font-style:italic;padding:10px;border-radius:10px 20px 10px 20px;"
#ggiraph(code = {print(gg)}, tooltip_extra_css = tooltip_css, tooltip_opacity = .75 )
x <- girafe(ggobj = gg)
x <- girafe_options(x,
opts_zoom(min = 1, max = 3), opts_hover(css =tooltip_css))xplot<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(month=as.Date(as.character(lubridate::floor_date(fecha, "month"))))%>%
dplyr::group_by(month)%>%
dplyr::summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = month, y = gasto_total)) +
geom_point()+
geom_line(size=1) +
theme_custom() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2019-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Mes") +
scale_x_date(breaks = "1 month", minor_breaks = "1 month", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot) plot2<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = day, y = gasto_total)) +
geom_line(size=1) +
theme_custom() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2020-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Día") +
scale_x_date(breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot2) tsData <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto))%>%
dplyr::mutate(covid=case_when(day>as.Date("2019-06-02")~1,TRUE~0))%>%
dplyr::mutate(covid=case_when(day>as.Date("2020-03-10")~covid+1,TRUE~covid))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()tsData_gastos <-ts(tsData$gasto_total, frequency=7)
mstsData_gastos <- forecast::msts(Gastos_casa$monto, seasonal.periods=c(7,30))
tsData_gastos = decompose(tsData_gastos)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
# Assuming your time series starts on "2019-03-03"
start_date <- as.Date("2019-03-03")
frequency <- 7 # Weekly data
num_periods <- length(tsData_gastos$x) # Total number of periods in your time series
# Generate sequence of dates
dates <- tsData$day# seq.Date(from = start_date, by = "day", length.out = num_periods)
# Create a data frame from the decomposed time series object
tsData_gastos_df <- data.frame(
day = dates,
Actual = as.numeric(tsData_gastos$x),
Seasonal = as.numeric(tsData_gastos$seasonal),
Trend = as.numeric(tsData_gastos$trend),
Random = as.numeric(tsData_gastos$random)
)
tsData_gastos_long <- tsData_gastos_df %>%
pivot_longer(cols = c("Actual", "Seasonal", "Trend", "Random"),
names_to = "Component", values_to = "Value")
# Plotting with facet_wrap
ggplot(tsData_gastos_long, aes(x = day, y = Value)) +
geom_line() +
theme_bw() +
labs(title = "Descomposición de los Gastos Diarios", x = "Date", y = "Value") +
scale_x_date(date_breaks = "3 months", date_labels = "%m %Y") +
facet_wrap(~ Component, scales = "free_y", ncol=1) +
theme(axis.text.x = element_text(angle = 90, hjust = 1))+
theme(strip.text = element_text(size = 12))
#tsData_gastos$trend
#Using the inputted variables, a Type-2 Sum Squares ANCOVA Lagged Dependent Variable model is fitted which estimates the difference in means between interrupted and non-interrupted time periods, while accounting for the lag of the dependent variable and any further specified covariates.
#Typically such analyses use Auto-regressive Integrated Moving Average (ARIMA) models to handle the serial dependence of the residuals of a linear model, which is estimated either as part of the ARIMA process or through a standard linear regression modeling process [9,17]. All such time series methods enable the effect of the event to be separated from general trends and serial dependencies in time, thereby enabling valid statistical inferences to be made about whether an intervention has had an effect on a time series.
#it uses Type-2 Sum Squares ANCOVA Lagged Dependent Variable model
#ITSA model da cuenta de observaciones autocorrelacionadas e impactos dinámicos mediante una regresión de deltas en rezagados. Una vez que se incorporan en el modelo, se controlan.
#residual autocorrelation assumptions
#TSA allows the model to account for baseline levels and trends present in the data therefore allowing us to attribute significant changes to the interruption
#RDestimate(all~agecell,data=metro_region,cutpoint = 21)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
itsa_metro_region_quar2<-
its.analysis::itsa.model(time = "day", depvar = "trend",data=tsdata_gastos_trend,
interrupt_var = "covid",
alpha = 0.05,no.plots = F, bootstrap = TRUE, Reps = 10000, print = F) 
print(itsa_metro_region_quar2)## [[1]]
## [1] "ITSA Model Fit"
##
## $aov.result
## Anova Table (Type II tests)
##
## Response: depvar
## Sum Sq Df F value Pr(>F)
## interrupt_var 8.8707e+08 2 6.8274 0.0012 **
## lag_depvar 1.5061e+11 1 2318.3024 <2e-16 ***
## Residuals 4.9177e+10 757
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $tukey.result
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: stats::aov(formula = x$depvar ~ x$interrupt_var)
##
## $`x$interrupt_var`
## diff lwr upr p adj
## 1-0 7228.838 -15.89653 14473.57 0.0506517
## 2-0 30489.364 23948.99638 37029.73 0.0000000
## 2-1 23260.526 19450.72049 27070.33 0.0000000
##
##
## $data
## depvar interrupt_var lag_depvar
## 2 19269.29 0 16010.00
## 3 24139.00 0 19269.29
## 4 23816.14 0 24139.00
## 5 26510.14 0 23816.14
## 6 23456.71 0 26510.14
## 7 24276.71 0 23456.71
## 8 18818.71 0 24276.71
## 9 18517.14 0 18818.71
## 10 15475.29 0 18517.14
## 11 16365.29 0 15475.29
## 12 12621.29 0 16365.29
## 13 12679.86 0 12621.29
## 14 13440.71 0 12679.86
## 15 15382.86 0 13440.71
## 16 13459.71 0 15382.86
## 17 14644.14 0 13459.71
## 18 13927.00 0 14644.14
## 19 22034.57 0 13927.00
## 20 20986.00 0 22034.57
## 21 20390.57 0 20986.00
## 22 22554.14 0 20390.57
## 23 21782.57 0 22554.14
## 24 22529.57 0 21782.57
## 25 24642.71 0 22529.57
## 26 17692.29 0 24642.71
## 27 19668.29 0 17692.29
## 28 28640.00 0 19668.29
## 29 28706.00 0 28640.00
## 30 28331.57 0 28706.00
## 31 25617.86 0 28331.57
## 32 27223.29 0 25617.86
## 33 31622.57 0 27223.29
## 34 32021.43 0 31622.57
## 35 33634.57 0 32021.43
## 36 30784.86 0 33634.57
## 37 34770.57 0 30784.86
## 38 38443.00 1 34770.57
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## 250 33729.86 2 26932.43
## 251 38081.43 2 33729.86
## 252 44028.00 2 38081.43
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## 255 58350.57 2 46558.86
## 256 78380.00 2 58350.57
## 257 78168.29 2 78380.00
## 258 70510.86 2 78168.29
## 259 72207.14 2 70510.86
## 260 67881.00 2 72207.14
## 261 69536.43 2 67881.00
## 262 62390.71 2 69536.43
## 263 50113.14 2 62390.71
## 264 45565.57 2 50113.14
## 265 45805.29 2 45565.57
## 266 41348.57 2 45805.29
## 267 51426.86 2 41348.57
## 268 47160.57 2 51426.86
## 269 51907.43 2 47160.57
## 270 49751.43 2 51907.43
## 271 54407.43 2 49751.43
## 272 54746.29 2 54407.43
## 273 61634.57 2 54746.29
## 274 58926.43 2 61634.57
## 275 69999.29 2 58926.43
## 276 63044.86 2 69999.29
## 277 63285.29 2 63044.86
## 278 61395.43 2 63285.29
## 279 67969.43 2 61395.43
## 280 60792.57 2 67969.43
## 281 56859.14 2 60792.57
## 282 44899.43 2 56859.14
## 283 43064.14 2 44899.43
## 284 62790.29 2 43064.14
## 285 69120.71 2 62790.29
## 286 69589.43 2 69120.71
## 287 66633.29 2 69589.43
## 288 65588.57 2 66633.29
## 289 70168.57 2 65588.57
## 290 74644.71 2 70168.57
## 291 52891.00 2 74644.71
## 292 41560.57 2 52891.00
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## 294 46520.00 2 34704.86
## 295 50231.00 2 46520.00
## 296 49216.71 2 50231.00
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## 299 84485.00 2 83720.71
## 300 89765.00 2 84485.00
## 301 87702.86 2 89765.00
## 302 82013.86 2 87702.86
## 303 85982.43 2 82013.86
## 304 57248.43 2 85982.43
## 305 52968.43 2 57248.43
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## 307 45493.29 2 52601.86
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## 316 35683.43 2 37990.71
## 317 65201.86 2 35683.43
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## 319 64589.14 2 62730.57
## 320 73744.86 2 64589.14
## 321 76477.71 2 73744.86
## 322 105647.43 2 76477.71
## 323 103790.29 2 105647.43
## 324 76122.29 2 103790.29
## 325 74746.14 2 76122.29
## 326 72865.71 2 74746.14
## 327 63652.57 2 72865.71
## 328 60358.29 2 63652.57
## 329 25957.14 2 60358.29
## 330 30178.43 2 25957.14
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## 333 32582.71 2 33337.29
## 334 39184.43 2 32582.71
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## 341 36489.29 2 35729.86
## 342 36785.14 2 36489.29
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## 344 39832.14 2 37787.71
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## 348 22759.57 2 33557.00
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## 381 44864.57 2 33035.71
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## 383 46969.57 2 48946.14
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## 385 56370.14 2 49249.57
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## 439 42020.86 2 34916.86
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## 441 37966.43 2 38303.00
## 442 41408.14 2 37966.43
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## 445 38114.00 2 43555.29
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## 467 52457.00 2 55515.57
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## 470 47788.57 2 50523.57
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## 507 42263.00 2 46545.43
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## 539 55512.00 2 46425.71
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## 544 49850.57 2 49056.29
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## 546 29799.43 2 39145.71
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## 553 50565.00 2 49242.43
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## 556 58787.86 2 49786.71
## 557 58060.86 2 58787.86
## 558 62179.43 2 58060.86
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## 560 70797.00 2 57333.86
## 561 89901.71 2 70797.00
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## 563 65466.00 2 78558.14
## 564 70525.00 2 65466.00
## 565 68377.86 2 70525.00
## 566 69736.29 2 68377.86
## 567 60085.86 2 69736.29
## 568 41757.00 2 60085.86
## 569 49780.29 2 41757.00
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## 571 57894.29 2 56540.29
## 572 60270.29 2 57894.29
## 573 61011.00 2 60270.29
## 574 57721.43 2 61011.00
## 575 71741.00 2 57721.43
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## 578 61092.29 2 52390.29
## 579 62814.00 2 61092.29
## 580 54908.29 2 62814.00
## 581 62082.00 2 54908.29
## 582 57017.71 2 62082.00
## 583 53634.43 2 57017.71
## 584 69169.00 2 53634.43
## 585 52488.14 2 69169.00
## 586 60895.57 2 52488.14
## 587 59856.57 2 60895.57
## 588 52670.00 2 59856.57
## 589 51874.57 2 52670.00
## 590 52190.57 2 51874.57
## 591 41562.43 2 52190.57
## 592 44764.14 2 41562.43
## 593 38612.71 2 44764.14
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## 595 53505.00 2 43473.14
## 596 45870.86 2 53505.00
## 597 52578.00 2 45870.86
## 598 55300.00 2 52578.00
## 599 61789.71 2 55300.00
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## 601 62902.29 2 57391.71
## 602 53250.43 2 62902.29
## 603 55402.57 2 53250.43
## 604 56291.29 2 55402.57
## 605 58933.57 2 56291.29
## 606 59590.71 2 58933.57
## 607 59065.00 2 59590.71
## 608 52399.57 2 59065.00
## 609 60483.43 2 52399.57
## 610 58262.71 2 60483.43
## 611 54939.71 2 58262.71
## 612 51169.00 2 54939.71
## 613 43113.29 2 51169.00
## 614 56289.71 2 43113.29
## 615 60739.86 2 56289.71
## 616 50363.14 2 60739.86
## 617 62270.86 2 50363.14
## 618 67061.57 2 62270.86
## 619 59609.00 2 67061.57
## 620 85054.00 2 59609.00
## 621 68023.29 2 85054.00
## 622 59242.29 2 68023.29
## 623 61535.14 2 59242.29
## 624 56215.86 2 61535.14
## 625 45152.29 2 56215.86
## 626 57409.57 2 45152.29
## 627 35151.43 2 57409.57
## 628 34991.43 2 35151.43
## 629 45944.71 2 34991.43
## 630 57944.71 2 45944.71
## 631 55706.29 2 57944.71
## 632 88593.71 2 55706.29
## 633 77359.43 2 88593.71
## 634 79878.71 2 77359.43
## 635 81753.00 2 79878.71
## 636 75716.00 2 81753.00
## 637 67381.43 2 75716.00
## 638 63528.57 2 67381.43
## 639 49682.86 2 63528.57
## 640 47815.00 2 49682.86
## 641 46546.14 2 47815.00
## 642 44808.71 2 46546.14
## 643 42959.57 2 44808.71
## 644 46023.86 2 42959.57
## 645 51309.57 2 46023.86
## 646 68447.29 2 51309.57
## 647 84959.29 2 68447.29
## 648 81666.29 2 84959.29
## 649 82700.86 2 81666.29
## 650 89422.14 2 82700.86
## 651 104812.71 2 89422.14
## 652 98812.71 2 104812.71
## 653 64779.86 2 98812.71
## 654 61862.86 2 64779.86
## 655 58376.43 2 61862.86
## 656 59503.57 2 58376.43
## 657 55429.43 2 59503.57
## 658 44454.57 2 55429.43
## 659 47184.00 2 44454.57
## 660 52126.71 2 47184.00
## 661 51202.00 2 52126.71
## 662 64437.14 2 51202.00
## 663 64297.14 2 64437.14
## 664 64628.57 2 64297.14
## 665 51413.14 2 64628.57
## 666 52969.43 2 51413.14
## 667 54135.29 2 52969.43
## 668 48799.43 2 54135.29
## 669 41907.86 2 48799.43
## 670 45382.00 2 41907.86
## 671 42633.29 2 45382.00
## 672 46624.71 2 42633.29
## 673 44051.86 2 46624.71
## 674 35852.86 2 44051.86
## 675 29737.71 2 35852.86
## 676 29734.86 2 29737.71
## 677 32881.71 2 29734.86
## 678 38298.57 2 32881.71
## 679 40886.14 2 38298.57
## 680 38601.86 2 40886.14
## 681 38628.86 2 38601.86
## 682 39142.57 2 38628.86
## 683 32666.14 2 39142.57
## 684 39911.57 2 32666.14
## 685 39336.29 2 39911.57
## 686 39678.86 2 39336.29
## 687 41963.14 2 39678.86
## 688 54220.57 2 41963.14
## 689 63901.86 2 54220.57
## 690 73116.00 2 63901.86
## 691 60863.86 2 73116.00
## 692 56293.86 2 60863.86
## 693 52725.00 2 56293.86
## 694 58625.00 2 52725.00
## 695 47513.00 2 58625.00
## 696 40300.14 2 47513.00
## 697 33312.43 2 40300.14
## 698 29556.71 2 33312.43
## 699 27816.71 2 29556.71
## 700 34120.29 2 27816.71
## 701 32132.57 2 34120.29
## 702 32902.57 2 32132.57
## 703 39694.14 2 32902.57
## 704 72501.29 2 39694.14
## 705 79551.14 2 72501.29
## 706 99637.71 2 79551.14
## 707 95424.29 2 99637.71
## 708 98395.14 2 95424.29
## 709 115594.71 2 98395.14
## 710 114267.57 2 115594.71
## 711 88353.29 2 114267.57
## 712 88750.86 2 88353.29
## 713 78835.71 2 88750.86
## 714 75519.14 2 78835.71
## 715 73202.86 2 75519.14
## 716 53433.29 2 73202.86
## 717 48165.71 2 53433.29
## 718 52163.14 2 48165.71
## 719 49306.86 2 52163.14
## 720 36846.86 2 49306.86
## 721 43220.57 2 36846.86
## 722 38952.29 2 43220.57
## 723 41522.29 2 38952.29
## 724 39090.00 2 41522.29
## 725 28452.57 2 39090.00
## 726 32975.00 2 28452.57
## 727 33690.71 2 32975.00
## 728 26405.29 2 33690.71
## 729 47087.43 2 26405.29
## 730 49660.29 2 47087.43
## 731 47409.71 2 49660.29
## 732 53881.71 2 47409.71
## 733 45189.57 2 53881.71
## 734 45503.86 2 45189.57
## 735 54640.14 2 45503.86
## 736 39131.29 2 54640.14
## 737 35024.14 2 39131.29
## 738 44755.43 2 35024.14
## 739 41063.29 2 44755.43
## 740 42783.29 2 41063.29
## 741 45952.57 2 42783.29
## 742 44937.43 2 45952.57
## 743 40838.43 2 44937.43
## 744 48838.43 2 40838.43
## 745 43139.14 2 48838.43
## 746 67134.29 2 43139.14
## 747 73224.29 2 67134.29
## 748 68770.71 2 73224.29
## 749 59539.29 2 68770.71
## 750 82179.86 2 59539.29
## 751 74252.14 2 82179.86
## 752 73015.00 2 74252.14
## 753 56116.43 2 73015.00
## 754 111885.00 2 56116.43
## 755 131425.14 2 111885.00
## 756 136678.00 2 131425.14
## 757 115531.29 2 136678.00
## 758 118310.86 2 115531.29
## 759 117449.43 2 118310.86
## 760 115193.57 2 117449.43
## 761 61025.43 2 115193.57
## 762 43913.86 2 61025.43
##
## $alpha
## [1] 0.05
##
## $itsa.result
## [1] "Significant variation between time periods with chosen alpha"
##
## $group.means
## interrupt_var count mean s.d.
## 1 0 37 22066.04 6308.636
## 2 1 120 29463.10 9187.258
## 3 2 605 52723.63 17658.623
##
## $dependent
## [1] 19269.29 24139.00 23816.14 26510.14 23456.71 24276.71 18818.71
## [8] 18517.14 15475.29 16365.29 12621.29 12679.86 13440.71 15382.86
## [15] 13459.71 14644.14 13927.00 22034.57 20986.00 20390.57 22554.14
## [22] 21782.57 22529.57 24642.71 17692.29 19668.29 28640.00 28706.00
## [29] 28331.57 25617.86 27223.29 31622.57 32021.43 33634.57 30784.86
## [36] 34770.57 38443.00 35073.00 31422.29 30103.29 19319.29 27926.29
## [43] 30715.43 31962.29 39790.14 39211.57 44548.57 49398.00 41039.00
## [50] 34821.29 29123.57 21275.71 28476.14 24561.86 20323.57 25370.00
## [57] 26811.86 27151.86 27623.29 22896.57 41889.29 44000.14 38558.00
## [64] 43373.86 49001.00 61213.29 58939.57 42046.86 39191.71 42646.43
## [71] 36121.57 30915.57 20273.43 23938.29 19274.29 21662.29 15819.00
## [78] 18126.14 17240.71 16127.71 13917.14 15379.86 19510.14 24567.29
## [85] 25700.43 25729.00 26435.00 31157.14 29818.43 30962.43 28746.71
## [92] 27830.71 28252.14 28717.57 21365.43 24816.86 16838.57 15529.14
## [99] 13286.29 13629.43 14404.86 19524.86 18475.71 22495.00 22254.57
## [106] 24173.29 27466.43 24602.43 20531.14 20846.43 23875.71 36312.71
## [113] 34244.00 36347.43 39779.71 42018.71 39372.57 33444.00 29255.86
## [120] 31640.14 29671.14 31023.71 39723.43 39314.14 38239.86 34649.43
## [127] 36688.43 42867.57 42226.86 32155.14 33603.00 37254.43 33145.57
## [134] 31299.43 30252.00 26310.71 27929.86 27666.14 25017.57 27335.00
## [141] 25760.71 18436.86 21906.00 19418.14 22826.14 23444.29 25264.86
## [148] 25473.29 27366.86 28855.86 32326.86 27141.43 26297.71 23499.14
## [155] 30246.29 39931.86 38020.43 35004.00 40750.86 42363.29 46273.57
## [162] 41083.29 35711.29 41921.71 60583.29 63115.57 61300.14 57666.43
## [169] 55834.00 58927.71 57810.57 48987.14 52219.29 56503.57 56545.00
## [176] 64705.57 53833.29 50114.00 39592.43 29907.29 33923.29 45489.00
## [183] 44866.29 51680.57 58257.00 70600.57 76648.00 69430.14 69651.57
## [190] 77745.14 72795.86 67670.71 55357.86 48524.00 50154.43 45111.57
## [197] 36147.00 43501.57 41472.43 41058.00 41605.57 49382.86 59558.57
## [204] 59134.57 61109.00 63004.43 67344.29 78180.86 69117.86 55597.57
## [211] 49426.14 39119.43 35636.86 39201.14 27777.00 47207.00 55587.29
## [218] 56619.71 82679.86 91259.57 93552.71 102242.71 91884.00 85013.86
## [225] 84535.29 80700.43 79740.57 85163.14 86724.86 80355.00 74875.14
## [232] 81347.00 66062.43 56946.43 47732.14 38129.71 42928.29 45392.57
## [239] 37895.43 30660.29 42430.86 35845.14 40350.43 31494.71 30013.29
## [246] 34197.57 37430.14 26932.43 33729.86 38081.43 44028.00 47139.71
## [253] 46558.86 58350.57 78380.00 78168.29 70510.86 72207.14 67881.00
## [260] 69536.43 62390.71 50113.14 45565.57 45805.29 41348.57 51426.86
## [267] 47160.57 51907.43 49751.43 54407.43 54746.29 61634.57 58926.43
## [274] 69999.29 63044.86 63285.29 61395.43 67969.43 60792.57 56859.14
## [281] 44899.43 43064.14 62790.29 69120.71 69589.43 66633.29 65588.57
## [288] 70168.57 74644.71 52891.00 41560.57 34704.86 46520.00 50231.00
## [295] 49216.71 76914.86 83720.71 84485.00 89765.00 87702.86 82013.86
## [302] 85982.43 57248.43 52968.43 52601.86 45493.29 42298.86 46423.71
## [309] 37898.00 36435.14 30209.57 34541.86 33604.71 37990.71 35683.43
## [316] 65201.86 62730.57 64589.14 73744.86 76477.71 105647.43 103790.29
## [323] 76122.29 74746.14 72865.71 63652.57 60358.29 25957.14 30178.43
## [330] 30681.57 33337.29 32582.71 39184.43 40415.71 34975.43 34076.14
## [337] 34221.14 28862.57 35729.86 36489.29 36785.14 37787.71 39832.14
## [344] 41917.86 41633.57 33557.00 22759.57 28877.86 27574.00 27104.71
## [351] 24376.14 29732.29 34030.00 39139.71 37066.57 38509.29 40957.29
## [358] 49423.00 50053.29 50284.14 53103.86 50223.00 49587.14 41167.71
## [365] 37958.71 33582.29 31039.43 26526.57 34869.43 37487.43 46514.43
## [372] 39613.43 38980.57 37306.14 36771.29 26317.00 31580.71 23626.57
## [379] 33035.71 44864.57 48946.14 46969.57 49249.57 56370.14 67228.71
## [386] 59457.29 53124.71 52814.14 61262.00 61861.14 71784.71 59313.29
## [393] 61107.00 60603.43 60012.57 58280.43 56862.71 41704.43 51533.00
## [400] 50388.71 49205.29 56533.29 47996.14 47207.57 45292.00 40343.43
## [407] 39004.86 36788.43 30027.57 39040.14 42390.14 36291.14 30668.29
## [414] 47693.00 52094.43 56592.57 47971.43 43762.43 42246.71 46352.43
## [421] 33094.86 32784.86 26212.43 32611.57 42144.86 50034.86 46332.00
## [428] 42976.29 39456.29 39328.29 35296.14 30875.43 27709.00 29513.29
## [435] 31630.43 29346.14 34916.86 42020.86 38303.00 37966.43 41408.14
## [442] 38988.14 43555.29 38114.00 27847.86 26517.00 39518.29 39153.71
## [449] 45623.14 40627.43 41027.71 42882.86 47139.43 35547.57 41099.00
## [456] 35859.57 44524.57 48554.29 51554.29 47810.29 50490.00 50720.71
## [463] 52720.71 52145.57 55515.57 52457.00 58239.57 50523.57 47788.57
## [470] 46170.00 42305.57 46605.57 55149.57 48769.57 50719.43 44753.71
## [477] 42898.00 46141.14 34022.57 26651.86 28791.86 31879.00 33584.71
## [484] 34690.43 27410.43 41755.00 49379.57 57198.86 51144.57 56677.43
## [491] 65416.43 69779.71 54046.00 43259.57 40998.57 41368.57 42274.29
## [498] 35962.71 38709.00 44778.14 51282.43 52094.86 52221.43 45011.43
## [505] 46545.43 42263.00 45417.43 45034.71 37840.57 39135.43 38191.14
## [512] 39456.86 42479.14 34282.57 28878.43 56227.14 65569.43 69751.29
## [519] 62171.71 63705.14 79257.86 87244.71 58568.00 52695.29 48911.00
## [526] 53924.00 53358.86 42121.14 47835.71 62329.29 56056.86 59946.43
## [533] 64511.57 61137.43 55448.71 47964.43 46425.71 55512.00 55226.29
## [540] 46709.14 49254.71 49056.29 49850.57 39145.71 29799.43 34769.86
## [547] 44061.57 43829.14 45782.00 38924.57 49242.43 50565.00 38864.43
## [554] 49786.71 58787.86 58060.86 62179.43 57333.86 70797.00 89901.71
## [561] 78558.14 65466.00 70525.00 68377.86 69736.29 60085.86 41757.00
## [568] 49780.29 56540.29 57894.29 60270.29 61011.00 57721.43 71741.00
## [575] 59576.00 52390.29 61092.29 62814.00 54908.29 62082.00 57017.71
## [582] 53634.43 69169.00 52488.14 60895.57 59856.57 52670.00 51874.57
## [589] 52190.57 41562.43 44764.14 38612.71 43473.14 53505.00 45870.86
## [596] 52578.00 55300.00 61789.71 57391.71 62902.29 53250.43 55402.57
## [603] 56291.29 58933.57 59590.71 59065.00 52399.57 60483.43 58262.71
## [610] 54939.71 51169.00 43113.29 56289.71 60739.86 50363.14 62270.86
## [617] 67061.57 59609.00 85054.00 68023.29 59242.29 61535.14 56215.86
## [624] 45152.29 57409.57 35151.43 34991.43 45944.71 57944.71 55706.29
## [631] 88593.71 77359.43 79878.71 81753.00 75716.00 67381.43 63528.57
## [638] 49682.86 47815.00 46546.14 44808.71 42959.57 46023.86 51309.57
## [645] 68447.29 84959.29 81666.29 82700.86 89422.14 104812.71 98812.71
## [652] 64779.86 61862.86 58376.43 59503.57 55429.43 44454.57 47184.00
## [659] 52126.71 51202.00 64437.14 64297.14 64628.57 51413.14 52969.43
## [666] 54135.29 48799.43 41907.86 45382.00 42633.29 46624.71 44051.86
## [673] 35852.86 29737.71 29734.86 32881.71 38298.57 40886.14 38601.86
## [680] 38628.86 39142.57 32666.14 39911.57 39336.29 39678.86 41963.14
## [687] 54220.57 63901.86 73116.00 60863.86 56293.86 52725.00 58625.00
## [694] 47513.00 40300.14 33312.43 29556.71 27816.71 34120.29 32132.57
## [701] 32902.57 39694.14 72501.29 79551.14 99637.71 95424.29 98395.14
## [708] 115594.71 114267.57 88353.29 88750.86 78835.71 75519.14 73202.86
## [715] 53433.29 48165.71 52163.14 49306.86 36846.86 43220.57 38952.29
## [722] 41522.29 39090.00 28452.57 32975.00 33690.71 26405.29 47087.43
## [729] 49660.29 47409.71 53881.71 45189.57 45503.86 54640.14 39131.29
## [736] 35024.14 44755.43 41063.29 42783.29 45952.57 44937.43 40838.43
## [743] 48838.43 43139.14 67134.29 73224.29 68770.71 59539.29 82179.86
## [750] 74252.14 73015.00 56116.43 111885.00 131425.14 136678.00 115531.29
## [757] 118310.86 117449.43 115193.57 61025.43 43913.86
##
## $interrupt_var
## [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [297] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [334] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [408] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [445] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [482] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [519] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [556] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [593] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [630] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [667] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [704] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [741] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## Levels: 0 1 2
##
## $residuals
## 2 3 4 5 6 7
## 1988.26450 4027.25028 -525.01180 2449.39348 -2943.80622 528.13291
## 8 9 10 11 12 13
## -5642.04687 -1203.28021 -3983.21897 -451.32976 -4968.30538 -1658.02530
## 14 15 16 17 18 19
## -948.03814 333.29122 -3276.62587 -421.92480 -2167.75799 6562.66056
## 20 21 22 23 24 25
## -1527.43335 -1212.16513 1468.54312 -1182.11590 235.00309 1699.36754
## 26 27 28 29 30 31
## -7086.35081 926.17930 8181.71402 455.67316 23.92280 -2364.59580
## 32 33 34 35 36 37
## 1597.72599 4602.67784 1180.70288 2447.43355 -1803.31455 4657.41058
## 38 39 40 41 42 43
## 4332.94245 -2226.60556 -2950.43466 -1098.74576 -10737.17853 7235.85408
## 44 45 46 47 48 49
## 2549.71479 1374.16810 8119.11494 741.95623 6581.45241 6795.63284
## 50 51 52 53 54 55
## -5775.15371 -4732.97681 -5030.53208 -7929.85642 6086.52968 -4081.41413
## 56 57 58 59 60 61
## -4920.09602 3807.33424 866.30799 -45.96214 130.17238 -5005.98324
## 62 63 64 65 66 67
## 18091.93877 3707.39705 -3568.05041 5974.37271 7418.88620 14743.93124
## 68 69 70 71 72 73
## 1863.70116 -13054.26515 -1237.88419 4696.55572 -4828.76198 -4367.84574
## 74 75 76 77 78 79
## -10488.51550 2419.16963 -5427.80251 1010.93712 -6906.35508 475.75111
## 80 81 82 83 84 85
## -2413.45855 -2757.45327 -4001.37093 -618.74899 2241.15192 3711.09237
## 86 87 88 89 90 91
## 452.04635 -503.53031 177.65506 4286.62850 -1153.32317 1153.36615
## 92 93 94 95 96 97
## -2055.92578 -1047.55150 169.43400 268.84683 -7487.52629 2349.32603
## 98 99 100 101 102 103
## -8626.56656 -3006.75884 -4112.36165 -1821.27061 -1343.86571 3102.66533
## 104 105 106 107 108 109
## -2393.25859 2537.22021 -1194.00594 933.52343 2560.24005 -3163.89380
## 110 111 112 113 114 115
## -4747.76136 -896.51544 1858.94087 11664.97010 -1205.42701 2694.70459
## 116 117 118 119 120 121
## 4300.13750 3558.15642 -1032.58464 -4662.94944 -3702.05730 2319.68043
## 122 123 124 125 126 127
## -1720.10015 1342.57126 8867.56113 902.46981 183.65362 -2473.74500
## 128 129 130 131 132 133
## 2683.58504 7091.83208 1084.45853 -8430.78776 1764.47335 4158.42073
## 134 135 136 137 138 139
## -3121.74567 -1399.29710 -843.32858 -3874.91009 1167.28641 -502.67275
## 140 141 142 143 144 145
## -2922.20518 1695.53926 -1891.46080 -7848.03203 1981.96810 -3518.88098
## 146 147 148 149 150 151
## 2049.85261 -291.89317 991.81381 -380.94561 1331.60311 1176.01375
## 152 153 154 155 156 157
## 3353.79949 -4846.23398 -1186.34171 -3252.13722 5925.59830 9751.19569
## 158 159 160 161 162 163
## -3599.10994 -4955.44000 3411.22133 32.43593 2542.30820 -6044.10729
## 164 165 166 167 168 169
## -6908.28225 3967.79239 17235.53254 3560.02091 -454.72800 -2511.72093
## 170 171 172 173 174 175
## -1188.22530 3496.97505 -307.09556 -8160.27224 2735.12356 4212.25466
## 176 177 178 179 180 181
## 532.72999 8657.32021 -9302.51911 -3579.09502 -10870.42245 -11417.45504
## 182 183 184 185 186 187
## 1010.20704 9087.97743 -1579.69765 5775.42285 6433.56301 13065.42780
## 188 189 190 191 192 193
## 8392.31737 -4077.80361 2412.41997 10313.67805 -1664.97098 -2491.60017
## 194 195 196 197 198 199
## -10353.20962 -6493.20349 1072.51152 -5386.39229 -9971.18214 5169.22648
## 200 201 202 203 204 205
## -3247.44937 -1899.54317 -992.03559 6309.67781 9730.72670 468.99749
## 206 207 208 209 210 211
## 2811.67512 2992.28893 5685.94375 12753.29749 -5721.39403 -11370.35616
## 212 213 214 215 216 217
## -5799.25534 -10746.01031 -5277.07747 1311.86307 -13207.90444 16144.09987
## 218 219 220 221 222 223
## 7649.19887 1403.24935 26566.71568 12512.88510 7354.44378 14052.82186
## 224 225 226 227 228 229
## -3853.26107 -1726.73703 3761.49255 342.28040 2713.04242 8969.26122
## 230 231 232 233 234 235
## 5821.40755 -1904.81707 -1852.37732 9378.80111 -11526.65543 -7367.82286
## 236 237 238 239 240 241
## -8664.75386 -10264.46540 2873.92976 1170.59896 -8466.80542 -9190.59030
## 242 243 244 245 246 247
## 8863.78899 -7944.80696 2280.25015 -10488.35851 -4278.49361 1192.43049
## 248 249 250 251 252 253
## 790.89987 -12514.34125 3400.47738 1848.40102 4015.58077 1962.62685
## 254 255 256 257 258 259
## -1320.79126 10975.40438 20763.58847 3156.07659 -4317.47560 4029.37816
## 260 261 262 263 264 265
## -1770.00907 3642.72616 -4940.74758 -11012.18093 -4896.53515 -707.20084
## 266 267 268 269 270 271
## -5372.10985 8576.88538 -4442.51168 4009.66549 -2269.03655 4259.47516
## 272 273 274 275 276 277
## 554.54078 7148.52502 -1542.17613 11882.73538 -4688.60167 1591.83105
## 278 279 280 281 282 283
## -506.84117 7708.52230 -5177.93225 -2878.17480 -11421.65946 -2869.79069
## 284 285 286 287 288 289
## 18450.31970 7648.35795 2619.01952 -744.20728 778.52376 6265.87057
## 290 291 292 293 294 295
## 6764.22881 -18877.06893 -11314.13721 -8329.23915 9440.17332 2889.58084
## 296 297 298 299 300 301
## -1347.75263 27231.30947 9980.99921 4834.31683 9450.52557 2802.63974
## 302 303 304 305 306 307
## -1095.36459 7814.17118 -24366.58043 -3690.75875 -340.09913 -7130.29890
## 308 309 310 311 312 313
## -4150.84841 2748.40804 -9359.79386 -3417.96647 -8373.02903 1366.23972
## 314 315 316 317 318 319
## -3333.54495 1866.37480 -4250.20422 27272.12951 -836.26346 3168.64909
## 320 321 322 323 324 325
## 10710.17192 5491.18201 32287.37728 5095.98968 -20959.05960 1694.78552
## 326 327 328 329 330 331
## 1009.55343 -6570.41479 -1862.97605 -33402.99248 696.09608 -2466.99805
## 332 333 334 335 336 337
## -248.26934 -3309.36029 3947.70828 -554.67360 -7064.34564 -3238.67833
## 338 339 340 341 342 343
## -2312.63796 -7797.14362 3724.12529 -1480.76569 -1844.48131 -1098.86534
## 344 345 346 347 348 349
## 74.81799 384.92168 -1710.83177 -9540.49767 -13323.32763 2172.65357
## 350 351 352 353 354 355
## -4445.00768 -3781.87792 -6102.86911 1623.07058 1268.91091 2646.00909
## 356 357 358 359 360 361
## -3864.98157 -621.71798 573.26745 6912.86454 190.57644 -125.97708
## 362 363 364 365 366 367
## 2493.23502 -2836.57760 -970.37596 -8837.55502 -4734.18090 -6323.55470
## 368 369 370 371 372 373
## -5065.43143 -7369.78682 4892.54057 264.66965 7017.90542 -7723.15173
## 374 375 376 377 378 379
## -2362.40807 -3487.19267 -2567.78863 -12557.54453 1785.84157 -10739.89992
## 380 381 382 383 384 385
## 5577.51087 9234.41523 3042.48316 -2478.98173 1517.69413 6658.05836
## 386 387 388 389 390 391
## 11332.32858 -5869.89877 -5452.89180 -263.54942 8454.04271 1716.12089
## 392 393 394 395 396 397
## 11119.32960 -9970.83911 2654.45963 593.02595 439.52660 -779.44978
## 398 399 400 401 402 403
## -692.77734 -14619.76129 8373.96206 -1306.55516 -1496.15794 6859.66389
## 404 405 406 407 408 409
## -8041.93436 -1415.89540 -2646.58314 -5931.45794 -2972.13607 -4025.99939
## 410 411 412 413 414 415
## -8861.86188 6022.59461 1545.06884 -7463.44612 -7789.24902 14118.98375
## 416 417 418 419 420 421
## 3734.24493 4409.69458 -8118.13900 -4839.57361 -2699.72120 2722.40902
## 422 423 424 425 426 427
## -14101.02423 -2896.66533 -9199.85521 2907.52025 6883.07407 6493.30174
## 428 429 430 431 432 433
## -4062.11450 -4201.85320 -4807.37532 -1878.21334 -5799.18667 -6717.93680
## 434 435 436 437 438 439
## -6044.92229 -1490.55560 -940.45640 -5063.50594 2491.13773 4756.90594
## 440 441 442 443 444 445
## -5130.85993 -2238.42809 1495.60250 -3913.56741 2755.37431 -6652.52944
## 446 447 448 449 450 451
## -12192.85076 -4607.44040 9549.71062 -2106.63251 4679.43069 -5935.05946
## 452 453 454 455 456 457
## -1195.93631 311.55363 2956.91136 -12331.82883 3287.26592 -6773.64458
## 458 459 460 461 462 463
## 6441.86162 2945.92007 2446.06515 -3903.47062 2027.95231 -68.69722
## 464 465 466 467 468 469
## 1730.92467 -581.24203 3288.27640 -2697.18022 5741.79697 -6996.43528
## 470 471 472 473 474 475
## -3029.99726 -2273.18857 -4731.86855 2924.43375 7733.83247 -6066.73343
## 476 477 478 479 480 481
## 1424.22980 -6234.95867 -2909.37895 1945.47390 -12989.80578 -9835.39625
## 482 483 484 485 486 487
## -1293.84299 -65.31566 -1040.82176 -1416.54067 -9656.86672 11010.47153
## 488 489 490 491 492 493
## 6176.61160 7373.86610 -5471.56251 5319.51398 9253.16156 6026.52155
## 494 495 496 497 498 499
## -13496.75841 -10618.26848 -3511.12663 -1177.42116 -593.05629 -7691.25138
## 500 501 502 503 504 505
## 536.70939 4220.67036 5453.83312 617.21198 38.17950 -7281.74929
## 506 507 508 509 510 511
## 514.22169 -5100.50417 1773.26468 -1349.10844 -8210.85937 -667.80336
## 512 513 514 515 516 517
## -2736.68794 -650.85025 1272.14751 -9549.31511 -7834.63793 24207.63888
## 518 519 520 521 522 523
## 9797.24008 5865.21066 -5346.35357 2770.02318 16990.93646 11470.07577
## 524 525 526 527 528 529
## -24143.31920 -5109.96513 -3793.72845 4505.96883 -413.02431 -11159.90529
## 530 531 532 533 534 535
## 4314.75502 13845.15301 -5015.11520 4322.13525 5509.13893 -1829.88494
## 536 537 538 539 540 541
## -4588.11591 -7131.68543 -2170.20831 8252.46911 75.20732 -8193.78927
## 542 543 544 545 546 547
## 1749.02230 -660.26541 306.35788 -11088.34588 -11137.33550 1950.45368
## 548 549 550 551 552 553
## 6925.29148 -1377.10177 777.62236 -7775.88593 8497.72971 859.11916
## 554 555 556 557 558 559
## -11990.12133 9094.25022 8609.25767 64.65775 4814.63735 -3607.96248
## 560 561 562 563 564 565
## 14063.61695 21475.43110 -6460.81253 -9700.92832 6728.75389 187.80920
## 566 567 568 569 570 571
## 3411.05695 -7419.18304 -17366.52787 6575.58886 6367.26953 1850.12891
## 572 573 574 575 576 577
## 3050.16376 1727.29371 -2205.59691 14671.00654 -9670.15844 -6290.42515
## 578 579 580 581 582 583
## 8652.45341 2816.37691 -6584.66677 7455.25466 -3839.48746 -2824.38064
## 584 585 586 587 588 589
## 15648.61478 -14524.20291 8370.74902 29.79704 -6254.39049 -808.19607
## 590 591 592 593 594 595
## 198.64313 -10703.94950 1728.43361 -7203.72200 2999.29565 8809.81261
## 596 597 598 599 600 601
## -7537.11782 5800.36916 2697.13560 6822.76042 -3211.63384 6118.65304
## 602 603 604 605 606 607
## -8319.20110 2215.69479 1235.24734 3105.67410 1467.96030 371.50960
## 608 609 610 611 612 613
## -5837.32984 8035.53151 -1206.10911 -2600.39227 -3485.04143 -8265.84539
## 614 615 616 617 618 619
## 11907.06712 4913.32462 -9328.39181 11591.62258 6040.34500 -5573.01892
## 620 621 622 623 624 625
## 26344.62824 -12785.37200 -6774.99353 3144.26683 -4166.39266 -10610.10100
## 626 627 628 629 630 631
## 11256.02844 -21647.71324 -2476.25072 8615.99691 11102.93763 -1557.63405
## 632 633 634 635 636 637
## 33273.89643 -6523.51322 5752.88362 5439.13965 -2225.69985 -5317.06478
## 638 639 640 641 642 643
## -1931.24724 -12430.70914 -2273.39831 -1919.99924 -2555.41025 -2895.57568
## 644 645 646 647 648 649
## 1774.71266 4399.05826 16946.06665 18573.75741 939.88848 4834.46969
## 650 651 652 653 654 655
## 10657.21778 20210.27240 843.37757 -27978.40802 -1337.46571 -2290.44499
## 656 657 658 659 660 661
## 1864.70266 -3188.37721 -10624.79270 1636.43019 4208.60321 -1008.91738
## 662 663 664 665 666 667
## 13029.35086 1394.47145 1847.49169 -11655.78655 1378.25649 1192.46093
## 668 669 670 671 672 673
## -5155.95705 -7413.27291 2046.28192 -3719.76687 2658.95284 -3380.50762
## 674 675 676 677 678 679
## -9344.95051 -8339.16408 -3030.94672 118.39188 2802.16607 685.13246
## 680 681 682 683 684 685
## -3846.48989 -1835.56051 -1345.29605 -8267.89160 4602.39242 -2265.63441
## 686 687 688 689 690 691
## -1423.42048 563.33786 10836.83705 9872.39986 10678.23062 -9576.50520
## 692 693 694 695 696 697
## -3505.37301 -3105.13065 5894.46434 -10341.75602 -7903.70864 -8626.97048
## 698 699 700 701 702 703
## -6313.77157 -4791.88892 3022.89326 -4439.54799 -1943.19443 4179.62282
## 704 705 706 707 708 709
## 31088.20489 9644.66720 23608.35363 1949.49821 8579.76832 23199.11489
## 710 711 712 713 714 715
## 6933.93912 -17827.70719 5076.73044 -5183.70795 111.14044 675.33657
## 716 717 718 719 720 721
## -17082.51309 -5179.97596 3392.40122 -2935.69889 -12914.98068 4280.39221
## 722 723 724 725 726 727
## -5523.54038 753.51667 -3910.84470 -12435.80412 1325.35803 -1886.71083
## 728 729 730 731 732 733
## -9793.74580 17215.87866 1826.04808 -2659.08046 5767.56766 -8545.58438
## 734 735 736 737 738 739
## -682.06893 8181.25589 -15262.57435 -5900.08982 7398.29844 -4745.58211
## 740 741 742 743 744 745
## 181.08799 1856.53319 -1911.17210 -5128.50843 6431.52196 -6215.85916
## 746 747 748 749 750 751
## 22729.18131 7979.11357 -1763.69549 -7127.14416 23531.03307 -4060.28751
## 752 753 754 755 756 757
## 1587.88404 -14236.21407 56208.96818 27313.44165 15595.45169 -10113.43167
## 758 759 760 761 762
## 11032.31331 7756.79380 6249.09765 -45959.80638 -16025.69972
##
## $fitted.values
## 2 3 4 5 6 7 8 9
## 17281.02 20111.75 24341.15 24060.75 26400.52 23748.58 24460.76 19720.42
## 10 11 12 13 14 15 16 17
## 19458.50 16816.62 17589.59 14337.88 14388.75 15049.57 16736.34 15066.07
## 18 19 20 21 22 23 24 25
## 16094.76 15471.91 22513.43 21602.74 21085.60 22964.69 22294.57 22943.35
## 26 27 28 29 30 31 32 33
## 24778.64 18742.11 20458.29 28250.33 28307.65 27982.45 25625.56 27019.89
## 34 35 36 37 38 39 40 41
## 30840.73 31187.14 32588.17 30113.16 34110.06 37299.61 34372.72 31202.03
## 42 43 44 45 46 47 48 49
## 30056.46 20690.43 28165.71 30588.12 31671.03 38469.62 37967.12 42602.37
## 50 51 52 53 54 55 56 57
## 46814.15 39554.26 34154.10 29205.57 22389.61 28643.27 25243.67 21562.67
## 58 59 60 61 62 63 64 65
## 25945.55 27197.82 27493.11 27902.55 23797.35 40292.75 42126.05 37399.48
## 66 67 68 69 70 71 72 73
## 41582.11 46469.35 57075.87 55101.12 40429.60 37949.87 40950.33 35283.42
## 74 75 76 77 78 79 80 81
## 30761.94 21519.12 24702.09 20651.35 22725.36 17650.39 19654.17 18885.17
## 82 83 84 85 86 87 88 89
## 17918.51 15998.61 17268.99 20856.19 25248.38 26232.53 26257.34 26870.51
## 90 91 92 93 94 95 96 97
## 30971.75 29809.06 30802.64 28878.27 28082.71 28448.72 28852.95 22467.53
## 98 99 100 101 102 103 104 105
## 25465.14 18535.90 17398.65 15450.70 15748.72 16422.19 20868.97 19957.78
## 106 107 108 109 110 111 112 113
## 23448.58 23239.76 24906.19 27766.32 25278.90 21742.94 22016.77 24647.74
## 114 115 116 117 118 119 120 121
## 35449.43 33652.72 35479.58 38460.56 40405.16 38106.95 32957.91 29320.46
## 122 123 124 125 126 127 128 129
## 31391.24 29681.14 30855.87 38411.67 38056.20 37123.17 34004.84 35775.74
## 130 131 132 133 134 135 136 137
## 41142.40 40585.93 31838.53 33096.01 36267.32 32698.73 31095.33 30185.62
## 138 139 140 141 142 143 144 145
## 26762.57 28168.82 27939.78 25639.46 27652.18 26284.89 19924.03 22937.02
## 146 147 148 149 150 151 152 153
## 20776.29 23736.18 24273.04 25854.23 26035.25 27679.84 28973.06 31987.66
## 154 155 156 157 158 159 160 161
## 27484.06 26751.28 24320.69 30180.66 41619.54 39959.44 37339.64 42330.85
## 162 163 164 165 166 167 168 169
## 43731.26 47127.39 42619.57 37953.92 43347.75 59555.55 61754.87 60178.15
## 170 171 172 173 174 175 176 177
## 57022.23 55430.74 58117.67 57147.42 49484.16 52291.32 56012.27 56048.25
## 178 179 180 181 182 183 184 185
## 63135.80 53693.10 50462.85 41324.74 32913.08 36401.02 46445.98 45905.15
## 186 187 188 189 190 191 192 193
## 51823.44 57535.14 68255.68 73507.95 67239.15 67431.46 74460.83 70162.31
## 194 195 196 197 198 199 200 201
## 65711.07 55017.20 49081.92 50497.96 46118.18 38332.34 44719.88 42957.54
## 202 203 204 205 206 207 208 209
## 42597.61 43073.18 49827.84 58665.57 58297.32 60012.14 61658.34 65427.56
## 210 211 212 213 214 215 216 217
## 74839.25 66967.93 55225.40 49865.44 40913.93 37889.28 40984.90 31062.90
## 218 219 220 221 222 223 224 225
## 47938.09 55216.46 56113.14 78746.69 86198.27 88189.89 95737.26 86740.59
## 226 227 228 229 230 231 232 233
## 80773.79 80358.15 77027.53 76193.88 80903.45 82259.82 76727.52 71968.20
## 234 235 236 237 238 239 240 241
## 77589.08 64314.25 56396.90 48394.18 40054.36 44221.97 46362.23 39850.88
## 242 243 244 245 246 247 248 249
## 33567.07 43789.95 38070.18 41983.07 34291.78 33005.14 36639.24 39446.77
## 250 251 252 253 254 255 256 257
## 30329.38 36233.03 40012.42 45177.09 47879.65 47375.17 57616.41 75012.21
## 258 259 260 261 262 263 264 265
## 74828.33 68177.76 69651.01 65893.70 67331.46 61125.32 50462.11 46512.49
## 266 267 268 269 270 271 272 273
## 46720.68 42849.97 51603.08 47897.76 52020.47 50147.95 54191.74 54486.05
## 274 275 276 277 278 279 280 281
## 60468.60 58116.55 67733.46 61693.45 61902.27 60260.91 65970.50 59737.32
## 282 283 284 285 286 287 288 289
## 56321.09 45933.93 44339.97 61472.36 66970.41 67377.49 64810.05 63902.70
## 290 291 292 293 294 295 296 297
## 67880.49 71768.07 52874.71 43034.10 37079.83 47341.42 50564.47 49683.55
## 298 299 300 301 302 303 304 305
## 73739.72 79650.68 80314.47 84900.22 83109.22 78168.26 81615.01 56659.19
## 306 307 308 309 310 311 312 313
## 52941.96 52623.58 46449.71 43675.31 47257.79 39853.11 38582.60 33175.62
## 314 315 316 317 318 319 320 321
## 36938.26 36124.34 39933.63 37929.73 63566.83 61420.49 63034.69 70986.53
## 322 323 324 325 326 327 328 329
## 73360.05 98694.30 97081.35 73051.36 71856.16 70222.99 62221.26 59360.14
## 330 331 332 333 334 335 336 337
## 29482.33 33148.57 33585.56 35892.07 35236.72 40970.39 42039.77 37314.82
## 338 339 340 341 342 343 344 345
## 36533.78 36659.72 32005.73 37970.05 38629.62 38886.58 39757.32 41532.94
## 346 347 348 349 350 351 352 353
## 43344.40 43097.50 36082.90 26705.20 32019.01 30886.59 30479.01 28109.22
## 354 355 356 357 358 359 360 361
## 32761.09 36493.71 40931.55 39131.00 40384.02 42510.14 49862.71 50410.12
## 362 363 364 365 366 367 368 369
## 50610.62 53059.58 50557.52 50005.27 42692.90 39905.84 36104.86 33896.36
## 370 371 372 373 374 375 376 377
## 29976.89 37222.76 39496.52 47336.58 41342.98 40793.34 39339.07 38874.54
## 378 379 380 381 382 383 384 385
## 29794.87 34366.47 27458.20 35630.16 45903.66 49448.55 47731.88 49712.08
## 386 387 388 389 390 391 392 393
## 55896.39 65327.18 58577.61 53077.69 52807.96 60145.02 60665.38 69284.12
## 394 395 396 397 398 399 400 401
## 58452.54 60010.40 59573.04 59059.88 57555.49 56324.19 43159.04 51695.27
## 402 403 404 405 406 407 408 409
## 50701.44 49673.62 56038.08 48623.47 47938.58 46274.89 41976.99 40814.43
## 410 411 412 413 414 415 416 417
## 38889.43 33017.55 40845.07 43754.59 38457.53 33574.02 48360.18 52182.88
## 418 419 420 421 422 423 424 425
## 56089.57 48602.00 44946.44 43630.02 47195.88 35681.52 35412.28 29704.05
## 426 427 428 429 430 431 432 433
## 35261.78 43541.56 50394.11 47178.14 44263.66 41206.50 41095.33 37593.37
## 434 435 436 437 438 439 440 441
## 33753.92 31003.84 32570.88 34409.65 32425.72 37263.95 43433.86 40204.86
## 442 443 444 445 446 447 448 449
## 39912.54 42901.71 40799.91 44766.53 40040.71 31124.44 29968.58 41260.35
## 450 451 452 453 454 455 456 457
## 40943.71 46562.49 42223.65 42571.30 44182.52 47879.40 37811.73 42633.22
## 458 459 460 461 462 463 464 465
## 38082.71 45608.37 49108.22 51713.76 48462.05 50789.41 50989.79 52726.81
## 466 467 468 469 470 471 472 473
## 52227.30 55154.18 52497.77 57520.01 50818.57 48443.19 47037.44 43681.14
## 474 475 476 477 478 479 480 481
## 47415.74 54836.30 49295.20 50988.67 45807.38 44195.67 47012.38 36487.25
## 482 483 484 485 486 487 488 489
## 30085.70 31944.32 34625.54 36106.97 37067.30 30744.53 43202.96 49824.99
## 490 491 492 493 494 495 496 497
## 56616.13 51357.91 56163.27 63753.19 67542.76 53877.84 44509.70 42545.99
## 498 499 500 501 502 503 504 505
## 42867.34 43653.97 38172.29 40557.47 45828.60 51477.65 52183.25 52293.18
## 506 507 508 509 510 511 512 513
## 46031.21 47363.50 43644.16 46383.82 46051.43 39803.23 40927.83 40107.71
## 514 515 516 517 518 519 520 521
## 41207.00 43831.89 36713.07 32019.50 55772.19 63886.08 67518.07 60935.12
## 522 523 524 525 526 527 528 529
## 62266.92 75774.64 82711.32 57805.25 52704.73 49418.03 53771.88 53281.05
## 530 531 532 533 534 535 536 537
## 43520.96 48484.13 61071.97 55624.29 59002.43 62967.31 60036.83 55096.11
## 538 539 540 541 542 543 544 545
## 48595.92 47259.53 55151.08 54902.93 47505.69 49716.55 49544.21 50234.06
## 546 547 548 549 550 551 552 553
## 40936.76 32819.40 37136.28 45206.24 45004.38 46700.46 40744.70 49705.88
## 554 555 556 557 558 559 560 561
## 50854.55 40692.46 50178.60 57996.20 57364.79 60941.82 56733.38 68426.28
## 562 563 564 565 566 567 568 569
## 85018.96 75166.93 63796.25 68190.05 66325.23 67505.04 59123.53 43204.70
## 570 571 572 573 574 575 576 577
## 50173.02 56044.16 57220.12 59283.71 59927.03 57069.99 69246.16 58680.71
## 578 579 580 581 582 583 584 585
## 52439.83 59997.62 61492.95 54626.75 60857.20 56458.81 53520.39 67012.35
## 586 587 588 589 590 591 592 593
## 52524.82 59826.77 58924.39 52682.77 51991.93 52266.38 43035.71 45816.44
## 594 595 596 597 598 599 600 601
## 40473.85 44695.19 53407.97 46777.63 52602.86 54966.95 60603.35 56783.63
## 602 603 604 605 606 607 608 609
## 61569.63 53186.88 55056.04 55827.90 58122.75 58693.49 58236.90 52447.90
## 610 611 612 613 614 615 616 617
## 59468.82 57540.11 54654.04 51379.13 44382.65 55826.53 59691.53 50679.23
## 618 619 620 621 622 623 624 625
## 61021.23 65182.02 58709.37 80808.66 66017.28 58390.88 60382.25 55762.39
## 626 627 628 629 630 631 632 633
## 46153.54 56799.14 37467.68 37328.72 46841.78 57263.92 55319.82 83882.94
## 634 635 636 637 638 639 640 641
## 74125.83 76313.86 77941.70 72698.49 65459.82 62113.57 50088.40 48466.14
## 642 643 644 645 646 647 648 649
## 47364.12 45855.15 44249.14 46910.51 51501.22 66385.53 80726.40 77866.39
## 650 651 652 653 654 655 656 657
## 78764.93 84602.44 97969.34 92758.27 63200.32 60666.87 57638.87 58617.81
## 658 659 660 661 662 663 664 665
## 55079.36 45547.57 47918.11 52210.92 51407.79 62902.67 62781.08 63068.93
## 666 667 668 669 670 671 672 673
## 51591.17 52942.82 53955.39 49321.13 43335.72 46353.05 43965.76 47432.36
## 674 675 676 677 678 679 680 681
## 45197.81 38076.88 32765.80 32763.32 35496.41 40201.01 42448.35 40464.42
## 682 683 684 685 686 687 688 689
## 40487.87 40934.03 35309.18 41601.92 41102.28 41399.80 43383.73 54029.46
## 690 691 692 693 694 695 696 697
## 62437.77 70440.36 59799.23 55830.13 52730.54 57854.76 48203.85 41939.40
## 698 699 700 701 702 703 704 705
## 35870.49 32608.60 31097.39 36572.12 34845.77 35514.52 41413.08 69906.48
## 706 707 708 709 710 711 712 713
## 76029.36 93474.79 89815.37 92395.60 107333.63 106180.99 83674.13 84019.42
## 714 715 716 717 718 719 720 721
## 75408.00 72527.52 70515.80 53345.69 48770.74 52242.56 49761.84 38940.18
## 722 723 724 725 726 727 728 729
## 44475.83 40768.77 43000.84 40888.38 31649.64 35577.43 36199.03 29871.55
## 730 731 732 733 734 735 736 737
## 47834.24 50068.79 48114.15 53735.16 46185.93 46458.89 54393.86 40924.23
## 738 739 740 741 742 743 744 745
## 37357.13 45808.87 42602.20 44096.04 46848.60 45966.94 42406.91 49355.00
## 746 747 748 749 750 751 752 753
## 44405.10 65245.17 70534.41 66666.43 58648.82 78312.43 71427.12 70352.64
## 754 755 756 757 758 759 760 761
## 55676.03 104111.70 121082.55 125644.72 107278.54 109692.63 108944.47 106985.23
## 762
## 59939.56
##
## $shapiro.test
## [1] 0
##
## $levenes.test
## [1] 0
##
## $autcorr
## [1] "No autocorrelation evidence"
##
## $post_sums
## [1] "Post-Est Warning"
##
## $adjr_sq
## [1] 0.8226
##
## $fstat.bootstrap
##
## ORDINARY NONPARAMETRIC BOOTSTRAP
##
##
## Call:
## boot::boot(data = x, statistic = f.stat, R = Reps, formula = depvar ~
## ., parallel = parr)
##
##
## Bootstrap Statistics :
## original bias std. error
## t1* 6.827427 0.5057291 3.209638
## t2* 2318.302417 33.1928151 337.304694
## WARNING: All values of t3* are NA
##
## $itsa.plot
##
## $booted.ints
## Parameter Lower CI Median F-value Upper CI
## 1 interrupt_var 2.758514 6.955915 13.20978
## 2 lag_depvar 1834.632059 2328.856867 2945.05210Ahora con las tendencias descompuestas
require(zoo)
require(scales)
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha2=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(treat=ifelse(fecha2>"2019-W26",1,0)) %>%
dplyr::mutate(gasto= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
gasto=="Tina"~"Electrodomésticos/mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"Donaciones/regalos",
gasto=="Regalo chocolates"~"Donaciones/regalos",
gasto=="filtro piscina msp"~"Electrodomésticos/mantención casa",
gasto=="Chromecast"~"Electrodomésticos/mantención casa",
gasto=="Muebles ratan"~"Electrodomésticos/mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"Electrodomésticos/mantención casa",
gasto=="Sopapo"~"Electrodomésticos/mantención casa",
gasto=="filtro agua"~"Electrodomésticos/mantención casa",
gasto=="ropa tami"~"Donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"Donaciones/regalos",
gasto=="Matri Andrés Kogan"~"Donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Transporte",
gasto=="Uber Reñaca"~"Transporte",
gasto=="filtro piscina mspa"~"Electrodomésticos/mantención casa",
gasto=="Limpieza Alfombra"~"Electrodomésticos/mantención casa",
gasto=="Aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Limpieza alfombras"~"Electrodomésticos/mantención casa",
gasto=="Pila estufa"~"Electrodomésticos/mantención casa",
gasto=="Reloj"~"Electrodomésticos/mantención casa",
gasto=="Arreglo"~"Electrodomésticos/mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
#2024
gasto=="cartero"~"Correo",
gasto=="correo"~"Correo",
gasto=="Gaviscón y Paracetamol"~"Farmacia",
gasto=="Regalo Matri Cony"~"Donaciones/regalos",
gasto=="Regalo Matri Chepa"~"Donaciones/regalos",
gasto=="Aporte Basureros"~"Donaciones/regalos",
gasto=="donación"~"Donaciones/regalos",
gasto=="Plata Reciclaje y Basurero"~"Donaciones/regalos",
gasto=="basureros"~"Donaciones/regalos",
gasto=="Microondas regalo"~"Donaciones/regalos",
gasto=="Cruz Verde"~"Farmacia",
gasto=="Remedios Covid"~"Farmacia",
gasto=="nacho"~"Electrodomésticos/mantención casa",
gasto=="Jardinero"~"Electrodomésticos/mantención casa",
gasto=="mantencion toyotomi"~"Electrodomésticos/mantención casa",
gasto=="Cámaras Seguridad M.Barrios"~"Electrodomésticos/mantención casa",
gasto=="Uber cumple papá"~"Transporte",
gasto=="Uber"~"Transporte",
gasto=="Uber Matri Cony"~"Transporte",
gasto=="Bencina + tag"~"Gas/Bencina",
gasto=="Bencina + Tag cumple Delox"~"Gas/Bencina",
gasto=="Bencina + peajes Maite"~"Gas/Bencina",
gasto=="Crunchyroll"~"Netflix",
gasto=="Crunchyroll"~"Netflix",
gasto=="Incoludido"~"Enceres",
gasto=="Cortina baño"~"Electrodomésticos/mantención casa",
gasto=="Forro cortina ducha"~"Electrodomésticos/mantención casa",
gasto=="Brussels"~"Comida",
gasto=="Tres toques"~"Enceres",
gasto=="Transferencia"~"Otros",
gasto=="prestamo"~"Otros",
gasto=="Préstamo Andrés"~"Otros",
gasto=="mouse"~"Otros",
gasto=="lamina"~"Otros",
T~gasto)) %>%
dplyr::group_by(gastador, fecha,gasto, .drop=F) %>%
#dplyr::mutate(fecha_simp=week(parse_date(fecha))) %>%
# dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::summarise(monto=sum(monto)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
ggplot2::ggplot(aes(x = fecha, y = monto, color=as.factor(gastador_nombre))) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(size=1) +
facet_grid(gasto~.)+
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") +
ggtitle( "Figura 6. Gastos Semanales por Gastador e ítem (media)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
guides(color = F)+
theme_custom() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
# Apply MSTL decomposition
mstl_data_autplt <- forecast::mstl(Gastos_casa$monto, lambda = "auto",iterate=5000000,start =
lubridate::decimal_date(as.Date("2019-03-03")))
# Convert the decomposed time series to a data frame
mstl_df <- data.frame(
Date = as.Date(Gastos_casa$fecha, format="%d/%m/%Y"),
Data = as.numeric(mstl_data_autplt[, "Data"]),
Trend = as.numeric(mstl_data_autplt[, "Trend"]),
Remainder = as.numeric(mstl_data_autplt[, "Remainder"])
)
# Reshape the data frame for ggplot2
mstl_long <- mstl_df %>%
pivot_longer(cols = -Date, names_to = "Component", values_to = "Value")
# Plotting with ggplot2
ggplot(mstl_long, aes(x = Date, y = Value)) +
geom_line() +
theme_bw() +
labs(title = "Descomposición MSTL", x = "Fecha", y = "Valor") +
scale_x_date(date_breaks = "3 months", date_labels = "%m-%Y") +
facet_wrap(~ Component, scales = "free_y", ncol = 1) +
theme(strip.text = element_text(size = 12),
axis.text.x = element_text(angle = 90, hjust = 1))
library(bsts)
library(CausalImpact)
ts_week_covid<-
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(fecha_week)%>%
dplyr::summarise(gasto_total=sum(monto,na.rm=T)/1000,min_day=min(day))%>%
dplyr::ungroup() %>%
dplyr::mutate(covid=dplyr::case_when(min_day>=as.Date("2020-03-17")~1,TRUE~0))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()
ts_week_covid$gasto_total_na<-ts_week_covid$gasto_total
post_resp<-ts_week_covid$gasto_total[which(ts_week_covid$covid==1)]
ts_week_covid$gasto_total_na[which(ts_week_covid$covid==1)]<-NA
ts_week_covid$gasto_total[which(ts_week_covid$covid==0)]## [1] 98.357 4.780 56.784 50.506 64.483 67.248 49.299 35.786 58.503
## [10] 64.083 20.148 73.476 127.004 81.551 69.599 134.446 58.936 26.145
## [19] 129.927 104.989 130.860 81.893 95.697 64.579 303.471 151.106 49.275
## [28] 76.293 33.940 83.071 119.512 20.942 58.055 71.728 44.090 33.740
## [37] 59.264 77.410 60.831 63.376 48.754 235.284 29.604 115.143 72.419
## [46] 5.980 80.063 149.178 69.918 107.601 72.724 63.203 99.681 130.309
## [55] 195.898 112.066# Model 1
ssd <- list()
# Local trend, weekly-seasonal #https://qastack.mx/stats/209426/predictions-from-bsts-model-in-r-are-failing-completely - PUSE UN GENERALIZED LOCAL TREND
ssd <- AddLocalLevel(ssd, ts_week_covid$gasto_total_na) #AddSemilocalLinearTrend #AddLocalLevel
# Add weekly seasonal
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na,nseasons=5, season.duration = 52) #weeks OJO, ESTOS NO SON WEEKS VERDADEROS. PORQUE TENGO MAS DE EUN AÑO
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na, nseasons = 12, season.duration =4) #years
# For example, to add a day-of-week component to data with daily granularity, use model.args = list(nseasons = 7, season.duration = 1). To add a day-of-week component to data with hourly granularity, set model.args = list(nseasons = 7, season.duration = 24).
model1d1 <- bsts(ts_week_covid$gasto_total_na,
state.specification = ssd, #A list with elements created by AddLocalLinearTrend, AddSeasonal, and similar functions for adding components of state. See the help page for state.specification.
family ="student", #A Bayesian Analysis of Time-Series Event Count Data. POISSON NO SE PUEDE OCUPAR
niter = 20000,
#burn = 200, #http://finzi.psych.upenn.edu/library/bsts/html/SuggestBurn.html Suggest the size of an MCMC burn in sample as a proportion of the total run.
seed= 2125)## =-=-=-=-= Iteration 0 Mon Sep 23 00:46:50 2024
## =-=-=-=-=
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## =-=-=-=-= Iteration 10000 Mon Sep 23 00:47:27 2024
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## =-=-=-=-=#,
# dynamic.regression=T)
#plot(model1d1, main = "Model 1")
#plot(model1d1, "components")
impact2d1 <- CausalImpact(bsts.model = model1d1,
post.period.response = post_resp)
plot(impact2d1)+
xlab("Date")+
ylab("Monto Semanal (En miles)")
burn1d1 <- SuggestBurn(0.1, model1d1)corpus <- Corpus(VectorSource(Gastos_casa$obs)) # formato de texto
d <- tm_map(corpus, tolower)
d <- tm_map(d, stripWhitespace)
d <- tm_map(d, removePunctuation)
d <- tm_map(d, removeNumbers)
d <- tm_map(d, removeWords, stopwords("spanish"))
d <- tm_map(d, removeWords, "menos")
tdm <- TermDocumentMatrix(d)
m <- as.matrix(tdm) #lo vuelve una matriz
v <- sort(rowSums(m),decreasing=TRUE) #lo ordena y suma
df <- data.frame(word = names(v),freq=v) # lo nombra y le da formato de data.frame
#findFreqTerms(tdm)
#require(devtools)
#install_github("lchiffon/wordcloud2")
#wordcloud2::wordcloud2(v, size=1.2)
wordcloud(words = df$word, freq = df$freq,
max.words=100, random.order=FALSE, rot.per=0.35,
colors=brewer.pal(8, "Dark2"), main="Figura 7. Nube de Palabras, Observaciones")
fit_month_gasto <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::mutate(gasto2= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
gasto=="Tina"~"Electrodomésticos/mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"Donaciones/regalos",
gasto=="Regalo chocolates"~"Donaciones/regalos",
gasto=="filtro piscina msp"~"Electrodomésticos/mantención casa",
gasto=="Chromecast"~"Electrodomésticos/mantención casa",
gasto=="Muebles ratan"~"Electrodomésticos/mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"Electrodomésticos/mantención casa",
gasto=="Sopapo"~"Electrodomésticos/mantención casa",
gasto=="filtro agua"~"Electrodomésticos/mantención casa",
gasto=="ropa tami"~"Donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"Donaciones/regalos",
gasto=="Matri Andrés Kogan"~"Donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Transporte",
gasto=="Uber Reñaca"~"Transporte",
gasto=="filtro piscina mspa"~"Electrodomésticos/mantención casa",
gasto=="Limpieza Alfombra"~"Electrodomésticos/mantención casa",
gasto=="Aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Limpieza alfombras"~"Electrodomésticos/mantención casa",
gasto=="Pila estufa"~"Electrodomésticos/mantención casa",
gasto=="Reloj"~"Electrodomésticos/mantención casa",
gasto=="Arreglo"~"Electrodomésticos/mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
#2024
gasto=="cartero"~"Correo",
gasto=="correo"~"Correo",
gasto=="Gaviscón y Paracetamol"~"Farmacia",
gasto=="Regalo Matri Cony"~"Donaciones/regalos",
gasto=="Regalo Matri Chepa"~"Donaciones/regalos",
gasto=="Aporte Basureros"~"Donaciones/regalos",
gasto=="donación"~"Donaciones/regalos",
gasto=="Plata Reciclaje y Basurero"~"Donaciones/regalos",
gasto=="basureros"~"Donaciones/regalos",
gasto=="Microondas regalo"~"Donaciones/regalos",
gasto=="Cruz Verde"~"Farmacia",
gasto=="Remedios Covid"~"Farmacia",
gasto=="nacho"~"Electrodomésticos/mantención casa",
gasto=="Jardinero"~"Electrodomésticos/mantención casa",
gasto=="mantencion toyotomi"~"Electrodomésticos/mantención casa",
gasto=="Cámaras Seguridad M.Barrios"~"Electrodomésticos/mantención casa",
gasto=="Uber cumple papá"~"Transporte",
gasto=="Uber"~"Transporte",
gasto=="Uber Matri Cony"~"Transporte",
gasto=="Bencina + tag"~"Gas/Bencina",
gasto=="Bencina + Tag cumple Delox"~"Gas/Bencina",
gasto=="Bencina + peajes Maite"~"Gas/Bencina",
gasto=="Crunchyroll"~"Netflix",
gasto=="Crunchyroll"~"Netflix",
gasto=="Incoludido"~"Enceres",
gasto=="Cortina baño"~"Electrodomésticos/mantención casa",
gasto=="Forro cortina ducha"~"Electrodomésticos/mantención casa",
gasto=="Brussels"~"Comida",
gasto=="Tres toques"~"Enceres",
gasto=="Transferencia"~"Otros",
gasto=="prestamo"~"Otros",
gasto=="Préstamo Andrés"~"Otros",
gasto=="mouse"~"Otros",
gasto=="lamina"~"Otros",
T~gasto)) %>%
dplyr::mutate(fecha_month=factor(fecha_month, levels=format(seq(from = as.Date("2019-03-03"), to = as.Date(substr(Sys.time(),1,10)), by = "1 month"),"%Y-%m")))%>%
dplyr::mutate(gasto2=factor(gasto2, levels=c("Agua", "Comida", "Comunicaciones","Electricidad", "Enceres", "Farmacia", "Gas/Bencina", "Diosi", "donaciones/regalos", "Electrodomésticos/ Mantención casa", "VTR", "Netflix", "Otros")))%>%
dplyr::group_by(fecha_month, gasto2, .drop=F)%>%
dplyr::summarise(gasto_total=sum(monto, na.rm = T)/1000)%>%
data.frame() %>% na.omit()
fit_month_gasto_24<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2024",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_23<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2023",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_22<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2022",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_21<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2021",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_20<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("202",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame() %>% ungroup()
fit_month_gasto_24 %>%
dplyr::right_join(fit_month_gasto_23,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_22,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_21,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_20,by="gasto2") %>%
janitor::adorn_totals() %>%
#dplyr::select(-3)%>%
knitr::kable(format = "markdown", size=12, col.names= c("Item","2024","2023","2022","2021","2020"))| Item | 2024 | 2023 | 2022 | 2021 | 2020 |
|---|---|---|---|---|---|
| Agua | 4.081250 | 5.195333 | 5.410333 | 5.849167 | 6.334807 |
| Comida | 332.209000 | 366.009167 | 312.386750 | 317.896583 | 342.752246 |
| Comunicaciones | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| Electricidad | 96.293625 | 38.104750 | 47.072333 | 29.523000 | 42.386053 |
| Enceres | 34.359750 | 18.259750 | 24.219750 | 14.801167 | 25.094561 |
| Farmacia | 0.000000 | 10.704083 | 2.835000 | 13.996083 | 8.751983 |
| Gas/Bencina | 62.041250 | 42.636000 | 45.575000 | 13.583667 | 34.125403 |
| Diosi | 38.008625 | 55.804250 | 31.180667 | 52.687833 | 42.554351 |
| donaciones/regalos | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| Electrodomésticos/ Mantención casa | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| VTR | 21.991250 | 12.829167 | 25.156667 | 19.086917 | 19.125754 |
| Netflix | 2.087125 | 8.713833 | 7.151583 | 7.028750 | 6.849018 |
| Otros | 102.375000 | 5.481667 | 5.000000 | 0.000000 | 16.575088 |
| Total | 693.446875 | 563.738000 | 505.988083 | 474.453167 | 544.549263 |
## Joining with `by = join_by(word)`
2. UF Proyectada
Saqué la UF proyectada
#options(max.print=5000)
uf18 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2018.htm")%>% rvest::html_nodes("table")
uf19 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2019.htm")%>% rvest::html_nodes("table")
uf20 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2020.htm")%>% rvest::html_nodes("table")
uf21 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2021.htm")%>% rvest::html_nodes("table")
uf22 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2022.htm")%>% rvest::html_nodes("table")
uf23 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2023.htm")%>% rvest::html_nodes("table")
tryCatch(uf24 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2024.htm")%>% rvest::html_nodes("table"),
error = function(c) {
uf24b <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
tryCatch(uf24 <-uf24[[length(uf24)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1),
error = function(c) {
uf24 <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
uf_serie<-
bind_rows(
cbind.data.frame(anio= 2018, uf18[[length(uf18)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2019, uf19[[length(uf19)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2020, uf20[[length(uf20)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2021, uf21[[length(uf21)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2022, uf22[[length(uf22)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2023, uf23[[length(uf23)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2024, uf24)
)
uf_serie_corrected<-
uf_serie %>%
dplyr::mutate(month=plyr::revalue(tolower(.[[3]]),c("ene" = 1, "feb"=2, "mar"=3, "abr"=4, "may"=5, "jun"=6, "jul"=7, "ago"=8, "sep"=9, "oct"=10, "nov"=11, "dic"=12))) %>%
dplyr::mutate(value=stringr::str_trim(value), value= sub("\\.","",value),value= as.numeric(sub("\\,",".",value))) %>%
dplyr::mutate(date=paste0(sprintf("%02d", .[[2]])," ",sprintf("%02d",as.numeric(month)),", ",.[[1]]), date3=lubridate::parse_date_time(date,c("%d %m, %Y"),exact=T),date2=date3) %>%
na.omit()#%>% dplyr::filter(is.na(date3))## Warning: There was 1 warning in `dplyr::mutate()`.
## i In argument: `date3 = lubridate::parse_date_time(date, c("%d %m, %Y"), exact
## = T)`.
## Caused by warning:
## ! 47 failed to parse.#Day of the month as decimal number (1–31), with a leading space for a single-digit number.
#Abbreviated month name in the current locale on this platform. (Also matches full name on input: in some locales there are no abbreviations of names.)
warning(paste0("number of observations:",nrow(uf_serie_corrected),", min uf: ",min(uf_serie_corrected$value),", min date: ",min(uf_serie_corrected $date3 )))## Warning: number of observations:2474, min uf: 26799.01, min date: 2018-01-01#
# uf_proyectado <- readxl::read_excel("uf_proyectado.xlsx") %>% dplyr::arrange(Período) %>%
# dplyr::mutate(Período= as.Date(lubridate::parse_date_time(Período, c("%Y-%m-%d"),exact=T)))
ts_uf_proy<-
ts(data = uf_serie_corrected$value,
start = as.numeric(as.Date("2018-01-01")),
end = as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])), frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats <- forecast::tbats(ts_uf_proy)
fr_fit_tbats<-forecast::forecast(fit_tbats, h=298)La proyección de la UF a 298 días más 2024-10-09 00:04:58 sería de: 38.249 pesos// Percentil 95% más alto proyectado: 41.264,1
Ahora con un modelo ARIMA automático
arima_optimal_uf = forecast::auto.arima(ts_uf_proy)
autoplotly::autoplotly(forecast::forecast(arima_optimal_uf, h=298), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq(from = as.Date("2018-01-01"),
to = as.Date("2018-01-01")+length(fit_tbats$fitted.values)+298, by = 90)),
tickvals = as.list(seq(from = as.numeric(as.Date("2018-01-01")),
to = as.numeric(as.Date("2018-01-01"))+length(fit_tbats$fitted.values)+298, by = 90)),
tickmode = "array",
tickangle = 90
))fr_fit_tbats_uf<-forecast::forecast(arima_optimal_uf, h=298)dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats)),variable) %>% dplyr::summarise(max=max(value)) %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_uf)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales",
col.names= c("Item","UF Proyectada (TBATS)","UF Proyectada (ARIMA)"))## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | UF Proyectada (TBATS) | UF Proyectada (ARIMA) |
|---|---|---|
| Lo.95 | 37947.23 | 37946.67 |
| Lo.80 | 37952.71 | 37952.01 |
| Point.Forecast | 38249.38 | 39073.92 |
| Hi.80 | 39961.78 | 43859.49 |
| Hi.95 | 40899.08 | 46392.82 |
3. Gastos proyectados
Lo haré en base a 2 cálculos: el gasto semanal y el gasto mensual en base a mis gastos desde marzo de 2019. La primera proyección la hice añadiendo el precio del arriendo mensual y partiendo en 2 (porque es con yo y Tami). No se incluye el último mes.
Gastos_casa_nvo <- readr::read_csv(as.character(path_sec),
col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador",
"link"),skip=1) %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))
Gastos_casa_m <-
Gastos_casa_nvo %>% dplyr::group_by(fecha_month)%>%
dplyr::summarise(gasto_total=(sum(monto)+500000)/1000,fecha=first(fecha))%>%
data.frame()
uf_serie_corrected_m <-
uf_serie_corrected %>% dplyr::mutate(ano_m=paste0(anio,"-",sprintf("%02d",as.numeric(month)))) %>% dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(value))/1000,fecha=first(date3))%>%
data.frame() %>%
dplyr::filter(fecha>="2019-02-28")
#Error: Error in standardise_path(file) : object 'enlace_gastos' not found
ts_uf_serie_corrected_m<-
ts(data = uf_serie_corrected_m$uf[-length(uf_serie_corrected_m$uf)],
start = 1,
end = nrow(uf_serie_corrected_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
ts_gastos_casa_m<-
ts(data = Gastos_casa_m$gasto_total[-length(Gastos_casa_m$gasto_total)],
start = 1,
end = nrow(Gastos_casa_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats_m <- forecast::tbats(ts_gastos_casa_m)
seq_dates<-format(seq(as.Date("2019/03/01"), by = "month", length = dim(Gastos_casa_m)[1]+12), "%m\n'%y")
autplo2t<-
autoplotly::autoplotly(forecast::forecast(fit_tbats_m, h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2tAhora asumiendo un modelo ARIMA, e incluimos como regresor al precio de la UF.
paste0("Optimo pero sin regresor")## [1] "Optimo pero sin regresor"arima_optimal = forecast::auto.arima(ts_gastos_casa_m)
arima_optimal## Series: ts_gastos_casa_m
## ARIMA(3,1,0)
##
## Coefficients:
## ar1 ar2 ar3
## -0.8424 -0.7260 -0.4724
## s.e. 0.1183 0.1313 0.1236
##
## sigma^2 = 35220: log likelihood = -438.29
## AIC=884.59 AICc=885.24 BIC=893.35paste0("Optimo pero con regresor")## [1] "Optimo pero con regresor"arima_optimal2 = forecast::auto.arima(ts_gastos_casa_m, xreg=as.numeric(ts_uf_serie_corrected_m[1:(length(Gastos_casa_m$gasto_total))]))
arima_optimal2## Series: ts_gastos_casa_m
## Regression with ARIMA(0,0,0) errors
##
## Coefficients:
## intercept xreg
## 471.3438 17.8651
## s.e. 204.5646 6.3520
##
## sigma^2 = 34928: log likelihood = -444.5
## AIC=895 AICc=895.38 BIC=901.61forecast_uf<-
cbind.data.frame(fecha=as.Date(seq(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])),(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)]))+299),by=1), origin = "1970-01-01"),forecast::forecast(fit_tbats, h=300)) %>%
dplyr::mutate(ano_m=stringr::str_extract(fecha,".{7}")) %>%
dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(`Hi 95`,na.rm=T))/1000,fecha=first(fecha))%>%
data.frame()
autplo2t2<-
autoplotly::autoplotly(forecast::forecast(arima_optimal2,xreg=c(forecast_uf$uf[1],forecast_uf$uf), h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2t2fr_fit_tbats_m<-forecast::forecast(fit_tbats_m, h=12)
fr_fit_tbats_m2<-forecast::forecast(arima_optimal, h=12)
fr_fit_tbats_m3<-forecast::forecast(arima_optimal2, h=12,xreg=c(forecast_uf$uf[1],forecast_uf$uf))
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m3)),variable) %>% dplyr::summarise(max=max(value)), dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m2)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales",
col.names= c("Item","Modelo ARIMA con regresor (UF)","Modelo ARIMA sin regresor","Modelo TBATS")) ## No id variables; using all as measure variables
## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | Modelo ARIMA con regresor (UF) | Modelo ARIMA sin regresor | Modelo TBATS |
|---|---|---|---|
| Lo.95 | 835.7739 | 963.7221 | 804.6098 |
| Lo.80 | 962.5634 | 1093.8587 | 900.4450 |
| Point.Forecast | 1202.0745 | 1339.6925 | 1113.7263 |
| Hi.80 | 1441.5856 | 1585.5263 | 1379.8025 |
| Hi.95 | 1568.3752 | 1715.6629 | 1546.3794 |
4. Gastos mensuales (resumen manual)
path_sec2<- paste0("https://docs.google.com/spreadsheets/d/",Sys.getenv("SUPERSECRET"),"/export?format=csv&id=",Sys.getenv("SUPERSECRET"),"&gid=847461368")
Gastos_casa_mensual_2022 <- readr::read_csv(as.character(path_sec2),
#col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador","link"),
skip=0)## Rows: 80 Columns: 4
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): mes_ano
## dbl (3): n, Tami, Andrés
##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.head(Gastos_casa_mensual_2022,5) %>%
knitr::kable("markdown",caption="Resumen mensual, primeras 5 observaciones")| n | mes_ano | Tami | Andrés |
|---|---|---|---|
| 1 | marzo_2019 | 175533 | 68268 |
| 2 | abril_2019 | 152640 | 55031 |
| 3 | mayo_2019 | 152985 | 192219 |
| 4 | junio_2019 | 291067 | 84961 |
| 5 | julio_2019 | 241389 | 205893 |
(
Gastos_casa_mensual_2022 %>%
reshape2::melt(id.var=c("n","mes_ano")) %>%
dplyr::mutate(gastador=as.factor(variable)) %>%
dplyr::select(-variable) %>%
ggplot2::ggplot(aes(x = n, y = value, color=gastador)) +
scale_color_manual(name="Gastador", values=c("red", "blue"))+
geom_line(size=1) +
#geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Meses", subtitle="Azul= Tami; Rojo= Andrés") +
ggtitle( "Gastos Mensuales (total manual)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
# scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
# scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
# guides(color = F)+
theme_custom() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
) %>% ggplotly()Session Info
Sys.getenv("R_LIBS_USER")## [1] "D:\\a\\_temp\\Library"sessionInfo()## R version 4.1.2 (2021-11-01)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows Server x64 (build 20348)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=Spanish_Chile.1252 LC_CTYPE=Spanish_Chile.1252
## [3] LC_MONETARY=Spanish_Chile.1252 LC_NUMERIC=C
## [5] LC_TIME=Spanish_Chile.1252
##
## attached base packages:
## [1] grid stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] CausalImpact_1.3.0 bsts_0.9.10 BoomSpikeSlab_1.2.6
## [4] Boom_0.9.15 scales_1.3.0 ggiraph_0.8.9
## [7] tidytext_0.4.1 DT_0.32 janitor_2.2.0
## [10] autoplotly_0.1.4 rvest_1.0.4 plotly_4.10.4
## [13] xts_0.13.2 forecast_8.21.1 wordcloud_2.6
## [16] RColorBrewer_1.1-3 SnowballC_0.7.1 tm_0.7-11
## [19] NLP_0.2-1 tsibble_1.1.4 lubridate_1.9.3
## [22] forcats_1.0.0 dplyr_1.1.4 purrr_1.0.2
## [25] tidyr_1.3.1 tibble_3.2.1 tidyverse_2.0.0
## [28] gsynth_1.2.1 lattice_0.20-45 GGally_2.2.1
## [31] ggplot2_3.5.0 gridExtra_2.3 plotrix_3.8-4
## [34] sparklyr_1.8.4 httr_1.4.7 readxl_1.4.3
## [37] zoo_1.8-12 stringr_1.5.1 stringi_1.8.3
## [40] data.table_1.15.0 reshape2_1.4.4 fUnitRoots_4021.80
## [43] plyr_1.8.9 readr_2.1.5
##
## loaded via a namespace (and not attached):
## [1] uuid_1.2-0 systemfonts_1.0.5 selectr_0.4-2
## [4] lazyeval_0.2.2 websocket_1.4.1 crosstalk_1.2.1
## [7] listenv_0.9.1 digest_0.6.34 foreach_1.5.2
## [10] htmltools_0.5.7 fansi_1.0.6 ggfortify_0.4.16
## [13] magrittr_2.0.3 doParallel_1.0.17 tzdb_0.4.0
## [16] globals_0.16.2 vroom_1.6.5 sandwich_3.1-0
## [19] askpass_1.2.0 timechange_0.3.0 anytime_0.3.9
## [22] tseries_0.10-55 colorspace_2.1-0 xfun_0.42
## [25] crayon_1.5.2 jsonlite_1.8.8 iterators_1.0.14
## [28] glue_1.7.0 gtable_0.3.4 car_3.1-2
## [31] quantmod_0.4.26 abind_1.4-5 mvtnorm_1.2-4
## [34] DBI_1.2.2 rngtools_1.5.2 Rcpp_1.0.12
## [37] lfe_2.9-0 viridisLite_0.4.2 xtable_1.8-4
## [40] bit_4.0.5 Formula_1.2-5 htmlwidgets_1.6.4
## [43] timeSeries_4032.109 gplots_3.1.3.1 ellipsis_0.3.2
## [46] spatial_7.3-14 farver_2.1.1 pkgconfig_2.0.3
## [49] nnet_7.3-16 sass_0.4.8 dbplyr_2.4.0
## [52] chromote_0.2.0 utf8_1.2.4 labeling_0.4.3
## [55] tidyselect_1.2.0 rlang_1.1.3 later_1.3.2
## [58] munsell_0.5.0 cellranger_1.1.0 tools_4.1.2
## [61] cachem_1.0.8 cli_3.6.2 generics_0.1.3
## [64] evaluate_0.23 fastmap_1.1.1 yaml_2.3.8
## [67] processx_3.8.3 knitr_1.45 bit64_4.0.5
## [70] caTools_1.18.2 future_1.33.1 nlme_3.1-153
## [73] doRNG_1.8.6 slam_0.1-50 xml2_1.3.6
## [76] tokenizers_0.3.0 compiler_4.1.2 rstudioapi_0.15.0
## [79] curl_5.2.0 bslib_0.6.1 highr_0.10
## [82] ps_1.7.6 fBasics_4032.96 Matrix_1.6-5
## [85] its.analysis_1.6.0 urca_1.3-3 vctrs_0.6.5
## [88] pillar_1.9.0 lifecycle_1.0.4 lmtest_0.9-40
## [91] jquerylib_0.1.4 bitops_1.0-7 R6_2.5.1
## [94] promises_1.2.1 KernSmooth_2.23-20 janeaustenr_1.0.0
## [97] parallelly_1.37.0 codetools_0.2-18 ggstats_0.5.1
## [100] assertthat_0.2.1 boot_1.3-28 gtools_3.9.5
## [103] MASS_7.3-54 openssl_2.1.1 withr_3.0.0
## [106] fracdiff_1.5-3 parallel_4.1.2 hms_1.1.3
## [109] quadprog_1.5-8 timeDate_4032.109 rmarkdown_2.25
## [112] snakecase_0.11.1 carData_3.0-5 TTR_0.24.4#save.image("__analisis.RData")
sesion_info <- devtools::session_info()
dplyr::select(
tibble::as_tibble(sesion_info$packages),
c(package, loadedversion, source)
) %>%
DT::datatable(filter = 'top', colnames = c('Row number' =1,'Variable' = 2, 'Percentage'= 3),
caption = htmltools::tags$caption(
style = 'caption-side: top; text-align: left;',
'', htmltools::em('Packages')),
options=list(
initComplete = htmlwidgets::JS(
"function(settings, json) {",
"$(this.api().tables().body()).css({
'font-family': 'Helvetica Neue',
'font-size': '50%',
'code-inline-font-size': '15%',
'white-space': 'nowrap',
'line-height': '0.75em',
'min-height': '0.5em'
});",#;
"}")))